医学
肺癌
优势比
危险系数
纵隔淋巴结
比例危险模型
置信区间
回顾性队列研究
逻辑回归
全肺切除术
外科
内科学
癌症
采样(信号处理)
队列
肿瘤科
转移
计算机视觉
滤波器(信号处理)
计算机科学
作者
Marcus K. Taylor,Matthew Evison,Bethan Clayton,Stuart W Grant,Glen P. Martin,Rajesh Shah,Piotr Krysiak,Kandadai S. Rammohan,Eustace Fontaine,Vijay Joshi,Felice Granato
标识
DOI:10.1016/j.jss.2021.09.014
摘要
Intraoperative mediastinal lymph node sampling (MLNS) is a crucial component of lung cancer surgery. Whilst several sampling strategies have been clearly defined in guidelines from international organizations, reports of adherence to these guidelines are lacking. We aimed to assess our center's adherence to guidelines and determine whether adequacy of sampling is associated with survival.A single-center retrospective review of consecutive patients undergoing lung resection for primary lung cancer between January 2013 and December 2018 was undertaken. Sampling adequacy was assessed against standards outlined in the International Association for the Study of Lung Cancer 2009 guidelines. Multivariable logistic and Cox proportional hazards regression analyses were used to assess the impact of specific variables on adequacy and of specific variables on overall survival, respectively.A total of 2380 patients were included in the study. Overall adequacy was 72.1% (n= 1717). Adherence improved from 44.8% in 2013 to 85.0% in 2018 (P< 0.001). Undergoing a right-sided resection increased the odds of adequate MLNS on multivariable logistic regression (odds ratio 1.666, 95% confidence interval [CI]: 1.385-2.003, P< 0.001). Inadequate MLNS was not significantly associated with reduced overall survival on log rank analysis (P= 0.340) or after adjustment with multivariable Cox proportional hazards (hazard ratio 0.839, 95% CI 0.643-1.093).Adherence to standards improved significantly over time and was significantly higher for right-sided resections. We found no evidence of an association between adequate MLNS and overall survival in this cohort. A pressing need remains for the introduction of national guidelines defining acceptable performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI